Distribution of nitrogen uptake, fibrous roots and nitrogen in the soil profile for fresh-market and processing carrot cultivars
Bibliographic record
Abstract
In temperate regions, yield of carrot [Daucus carota subsp. sativus (Hoffm.) Arkang.] is not generally affected by preplant applications of nitrogen (N). Previous studies speculate that carrots utilize N from much deeper in the soil profile than other crops. Carrot (cvs. Idaho and Fontana) were grown on organic and mineral soil in Ontario over a 3-yr period to determine rooting and N-uptake dynamics. Nitrogen application rates ranged from 0 to 200% of Ontario recommendations for each soil type. Soil samples collected at seeding and harvest at three depths were assessed for total N, nitrate-N and ammonium-N. The same cultivars were grown in 150-cm-deep PVC pipes filled with silica sand or soilless mix to assess root distribution and N uptake. Potassium nitrate fertilizer enriched with 15N was applied at three depths in the pipes and plants were assessed 28 d later for fertilizer N recovery. In the field, the highest N concentration both at seeding and at harvest was in the top 30 cm of the soil profile, but a significant pool of N was available at the 30–60 and 60–90 cm depths. In the pipes, up to 55% of the roots were below 30 cm depth, and these roots assimilated as much N as shallow roots. There were no differences between the cultivars in root distribution or timing of N uptake. Soil N at depths below 30 cm is available to carrots and contributes to plant growth. Soil sampling for N in the 30–60 cm depth is necessary for the determination of N fertilizer requirements. Key words: Daucus carota, rooting depth, fibrous roots, nitrate, ammonium, cultivar comparison
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".